A method, computer system, and a computer program product for qualifying an Internet of Things (IoT) event as a major event is provided. The present invention may include detecting the IoT event by utilizing one or more IoT sensors associated with a physical asset. The present invention may then include comparing the detected IoT event with one or more owner preferences associated the physical asset. The present invention may further include qualifying the compared IoT event as a major event based on the detected IoT event matching the one or more owner preferences.
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1. A computer-implemented method comprising: detecting an Internet of Things (IoT) event by utilizing one or more IoT sensors associated with a physical asset; comparing the detected IoT event with one or more owner preferences associated the physical asset; qualifying the compared IoT event as a major event based on the detected IoT event matching the one or more owner preferences; and computing an urgency score for each detected IoT event based on an average cost of repairs for one or more asset parts associated with the detected IoT event or a level of significance associated with the one or more asset parts associated with the detected IoT event.
This invention relates to a computer-implemented method for monitoring and managing Internet of Things (IoT) devices associated with physical assets, such as machinery or equipment. The method addresses the challenge of efficiently identifying and prioritizing IoT events that require immediate attention, particularly in environments where numerous sensors generate data that must be filtered and assessed for relevance. The method involves detecting an IoT event using one or more IoT sensors connected to a physical asset. Once detected, the event is compared against predefined owner preferences, which may include thresholds, conditions, or rules set by the asset owner. If the event matches these preferences, it is classified as a major event, indicating a significant issue that requires action. The method then computes an urgency score for the event based on either the average repair cost of the affected asset parts or the level of significance of those parts. This scoring helps prioritize maintenance or intervention efforts by quantifying the potential impact of the event. The system ensures that only relevant events are flagged, reducing unnecessary alerts while ensuring critical issues are addressed promptly. By integrating owner preferences and cost or significance-based scoring, the method provides a tailored and efficient approach to asset management.
2. The method of claim 1 , further comprising: in response to determining that the compared IoT event fails to match the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as a minor event; and performing one or more default choices, wherein the compared IoT event is saved in a traditional database.
This invention relates to Internet of Things (IoT) event processing systems, specifically for managing and responding to IoT events based on predefined owner preferences associated with physical assets. The problem addressed is the need for automated, preference-based event handling in IoT environments to ensure appropriate responses while minimizing unnecessary actions for minor events. The method involves monitoring IoT events generated by physical assets and comparing them against stored owner preferences. If an event does not match the preferences, it is classified as a minor event. In response, the system performs default actions, such as saving the event in a traditional database, rather than triggering more significant responses. This ensures that only relevant events receive priority processing, improving system efficiency and reducing unnecessary resource consumption. The system may also include additional steps, such as analyzing event data, determining event severity, and applying predefined rules to decide whether an event should be escalated or handled as a minor event. The use of a traditional database for storing minor events allows for historical tracking and future reference without requiring real-time intervention. This approach optimizes resource allocation and ensures that critical events are prioritized while minor events are managed efficiently.
3. The method of claim 1 , wherein the IoT event is selected from a group consisting of: (i) an on/off trigger; (ii) a threshold IoT sensor reading; (iii) an anomaly to a predicted sensor reading; (iv) a location event; and (v) a urgency score.
This invention relates to methods for processing Internet of Things (IoT) events in a system where IoT devices generate data that triggers actions or alerts. The problem addressed is the need for a flexible and efficient way to detect and respond to various types of IoT events, ensuring timely and appropriate reactions to changes in the IoT environment. The method involves monitoring IoT devices and detecting specific events that meet predefined criteria. These events include binary on/off triggers, such as a device being turned on or off, threshold sensor readings where a sensor value crosses a specified limit, anomalies detected in sensor data compared to predicted readings, location-based events such as a device entering or exiting a geographic area, and urgency scores that prioritize events based on their criticality. When an event is detected, the system processes it to determine the appropriate response, which may include sending alerts, adjusting device settings, or logging the event for further analysis. The method ensures that different types of IoT events are handled according to their specific characteristics, improving system responsiveness and reliability.
4. The method of claim 1 , further comprising: continuously monitoring the one or more IoT sensors associated with the physical asset, wherein the one or more IoT sensors are selected from a group consisting of: (i) a current health status associated with the physical asset; (ii) a current status of one or more asset parts associated with the physical asset; (iii) an identifier of asset parts associated with the physical asset; (iv) a historical usage of the physical asset; (v) a predicted maintenance associated with the physical asset; (vi) an anomaly associated with a performance of the physical asset; (vii) any recommended preventive maintenance schedules; (viii) any failure predictions associated with the physical asset; and (ix) one or more digital twins associated with a function and the performance of the physical asset.
This invention relates to monitoring and managing physical assets using Internet of Things (IoT) sensors to enhance maintenance and operational efficiency. The method involves continuously tracking various data points from IoT sensors associated with the asset, including its current health status, the status and identifiers of its components, historical usage patterns, predicted maintenance needs, performance anomalies, recommended preventive maintenance schedules, failure predictions, and digital twin models representing the asset's function and performance. By collecting and analyzing this data, the system enables proactive maintenance, reduces downtime, and improves asset reliability. The digital twin models provide real-time simulations to optimize performance and predict potential issues before they occur. This approach integrates multiple sensor inputs to create a comprehensive monitoring framework, ensuring assets operate at peak efficiency while minimizing maintenance costs and unplanned failures. The system is particularly useful in industrial settings where asset performance directly impacts productivity and safety.
5. The method of claim 1 , further comprising: initiating one or more digital twins associated with the physical asset, wherein the one or more digital twins are captured to digitally mimic the IoT event of the physical asset.
This invention relates to digital twin technology for monitoring and analyzing physical assets using Internet of Things (IoT) data. The problem addressed is the need for accurate, real-time digital representations of physical assets to improve operational efficiency, predictive maintenance, and decision-making. The method involves capturing IoT event data from a physical asset, such as sensor readings, operational status, or environmental conditions. This data is processed to generate a digital twin—a virtual model that dynamically mimics the physical asset's behavior. The digital twin is continuously updated with real-time IoT data to ensure synchronization with the physical asset's state. Additionally, the method includes initiating one or more digital twins to digitally replicate the IoT events of the physical asset. These digital twins can simulate different scenarios, predict failures, or optimize performance without affecting the actual asset. The digital twins can be used for testing, training, or decision support, enabling proactive maintenance and improved asset management. The technology is applicable in industries such as manufacturing, energy, transportation, and healthcare, where real-time monitoring and predictive analytics enhance operational reliability and efficiency. The digital twin approach reduces downtime, minimizes costs, and improves overall system performance by leveraging IoT data for accurate virtual modeling.
6. The method of claim 1 , further comprising: in response to determining that the compared IoT event matches the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as the major event; and performing one or more default choices, wherein the compared IoT event is saved to a blockchain structure.
This invention relates to Internet of Things (IoT) event processing systems, specifically for managing and qualifying IoT events based on owner preferences and storing validated events in a blockchain structure. The problem addressed is the need for automated, secure, and preference-based event qualification in IoT networks to ensure only relevant events are recorded and stored in a tamper-proof manner. The method involves comparing an IoT event to predefined owner preferences associated with a physical asset. If the event matches these preferences, it is qualified as a major event. Upon qualification, one or more default actions are performed, including saving the event to a blockchain structure. The blockchain ensures immutability and traceability of the event data, enhancing security and reliability. The system may also include generating a notification for the owner or a designated recipient upon qualification of the event. The blockchain structure may be part of a distributed ledger system, where the event data is cryptographically linked to previous entries, preventing unauthorized modifications. This approach ensures that only events meeting specific criteria are recorded, reducing data clutter and improving decision-making based on verified IoT data. The method is particularly useful in applications requiring high integrity, such as industrial monitoring, asset tracking, and smart infrastructure management.
7. The method of claim 1 , further comprising: in response to determining that the compared IoT event fails to match the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as a minor event; requesting an independent decision from a digital specialist to determine how to treat the IoT event, wherein a urgency score associated with IoT event is considered high; and in response to receiving the independent decision from the digital specialist, performing an action associated with the IoT event based on the received independent decision.
This invention relates to a system for managing Internet of Things (IoT) events associated with physical assets, particularly when those events do not align with predefined owner preferences. The system monitors IoT events generated by physical assets and compares them against stored owner preferences to determine whether the events require intervention. When an event does not match the owner's preferences, the system classifies it as a minor event but assigns it a high urgency score. The system then requests an independent decision from a digital specialist, who evaluates the event and determines the appropriate action. Upon receiving the specialist's decision, the system executes the corresponding action, such as alerting the owner, adjusting asset settings, or triggering maintenance. This approach ensures that even minor but urgent events are properly assessed and addressed, improving asset management and reducing potential risks. The system may also include additional features, such as generating alerts, logging events, or adjusting asset configurations based on the specialist's input. The overall goal is to enhance decision-making for IoT event management by incorporating human expertise when automated rules are insufficient.
8. A computer system for qualifying an Internet of Things (IoT) event as a major event, comprising: one or more processors, one or more computer-readable memories, one or more non-transitory computer-readable tangible storage media, and program instructions stored on at least one of the one or more non-transitory computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising: detecting an Internet of Things (IoT) event by utilizing one or more IoT sensors associated with a physical asset; comparing the detected IoT event with one or more owner preferences associated the physical asset; qualifying the compared IoT event as a major event based on the detected IoT event matching the one or more owner preferences; and computing an urgency score for each detected IoT event based on an average cost of repairs for one or more asset parts associated with the detected IoT event or a level of significance associated with the one or more asset parts associated with the detected IoT event.
A computer system monitors and evaluates events from Internet of Things (IoT) sensors connected to physical assets, such as machinery or equipment, to determine if an event qualifies as a major event based on predefined owner preferences. The system detects IoT events using sensors associated with the asset, then compares these events against owner-defined criteria or thresholds. If the event matches the preferences, it is classified as a major event. The system further calculates an urgency score for each event, which is derived from either the average repair cost of affected asset parts or the significance level of those parts. This urgency score helps prioritize responses to events based on their potential impact. The system leverages processing capabilities, memory, and storage to execute these functions, ensuring efficient event assessment and prioritization for maintenance or operational decisions. The approach aims to enhance asset management by automating event qualification and urgency assessment, reducing manual intervention and improving response times to critical issues.
9. The computer system of claim 8 , further comprising: in response to determining that the compared IoT event fails to match the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as a minor event; and performing one or more default choices, wherein the compared IoT event is saved in a traditional database.
This invention relates to a computer system for managing Internet of Things (IoT) events associated with physical assets, addressing the challenge of efficiently processing and categorizing IoT data based on owner preferences. The system monitors IoT events generated by physical assets and compares them against predefined owner preferences to determine whether the events meet specified criteria. If an IoT event does not match the owner's preferences, it is classified as a minor event. In response, the system performs default actions, such as saving the event in a traditional database for later review or analysis. The system may also include a machine learning model trained to predict future IoT events based on historical data, allowing for proactive management. Additionally, the system can generate alerts or notifications when events deviate significantly from expected behavior, ensuring timely intervention. The invention aims to streamline IoT event processing, reduce manual oversight, and enhance decision-making by automating event classification and response.
10. The computer system of claim 8 , wherein the IoT event is selected from a group consisting of: (i) an on/off trigger; (ii) a threshold IoT sensor reading; (iii) an anomaly to a predicted sensor reading; (iv) a location event; and (v) a urgency score.
This invention relates to an Internet of Things (IoT) event monitoring system that processes and analyzes IoT device data to detect and respond to specific events. The system collects sensor readings and other data from IoT devices and evaluates this data to identify predefined events, such as on/off triggers, threshold sensor readings, anomalies in predicted sensor values, location-based events, or urgency scores. The system includes a data processing module that receives and processes IoT data, an event detection module that identifies the occurrence of predefined events based on the processed data, and an event response module that generates alerts or triggers actions in response to detected events. The event detection module compares sensor readings against thresholds, detects deviations from predicted values, tracks location changes, or calculates urgency scores to determine event occurrences. The system may also include a machine learning model trained to predict sensor readings or detect anomalies, improving event detection accuracy. The response module can send notifications, adjust device settings, or escalate alerts based on the detected event type and severity. This system enhances IoT device monitoring by automating event detection and response, reducing manual oversight and improving system efficiency.
11. The computer system of claim 8 , further comprising: continuously monitoring the one or more IoT sensors associated with the physical asset, wherein the one or more IoT sensors are selected from a group consisting of: (i) a current health status associated with the physical asset; (ii) a current status of one or more asset parts associated with the physical asset; (iii) an identifier of asset parts associated with the physical asset; (iv) a historical usage of the physical asset; (v) a predicted maintenance associated with the physical asset; (vi) an anomaly associated with a performance of the physical asset; (vii) any recommended preventive maintenance schedules; (viii) any failure predictions associated with the physical asset; and (ix) one or more digital twins associated with a function and the performance of the physical asset.
This invention relates to a computer system for monitoring and managing physical assets using Internet of Things (IoT) sensors. The system addresses the challenge of maintaining and optimizing asset performance by continuously tracking various parameters through IoT sensors. These sensors collect data on the current health status of the asset, the status and identifiers of its parts, historical usage patterns, predicted maintenance needs, performance anomalies, recommended preventive maintenance schedules, failure predictions, and digital twin models representing the asset's function and performance. The system integrates these data points to provide real-time insights and proactive maintenance recommendations, improving asset reliability and operational efficiency. By leveraging IoT sensors, the system enables continuous monitoring and predictive analytics, reducing downtime and maintenance costs. The digital twin models further enhance decision-making by simulating asset behavior and performance under different conditions. This approach ensures comprehensive asset management, combining real-time data with predictive capabilities to optimize maintenance and performance.
12. The computer system of claim 8 , further comprising: initiating one or more digital twins associated with the physical asset, wherein the one or more digital twins are captured to digitally mimic the IoT event of the physical asset.
A computer system monitors and analyzes physical assets using Internet of Things (IoT) data. The system collects real-time IoT event data from sensors or devices associated with the physical asset, such as machinery, equipment, or infrastructure. The system processes this data to detect anomalies, predict failures, or optimize performance. To enhance this functionality, the system creates one or more digital twins of the physical asset. These digital twins are digital representations that mimic the behavior, characteristics, and IoT events of the physical asset. The digital twins allow for simulation, testing, and analysis of the asset's performance under various conditions without affecting the physical asset. This enables predictive maintenance, performance optimization, and scenario testing to improve operational efficiency and reduce downtime. The system may use machine learning or other analytical techniques to refine the digital twin models over time, ensuring they accurately reflect the physical asset's state and behavior. The digital twins can be updated dynamically as new IoT data is received, maintaining their accuracy and relevance. This approach supports data-driven decision-making and proactive asset management.
13. The computer system of claim 8 , further comprising: in response to determining that the compared IoT event matches the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as the major event; and performing one or more default choices, wherein the compared IoT event is saved to a blockchain structure.
The invention relates to a computer system for managing Internet of Things (IoT) events associated with physical assets, particularly focusing on event qualification and blockchain-based storage. The system addresses the challenge of efficiently processing and validating IoT-generated data to ensure accurate event tracking and secure storage. The system monitors IoT events related to physical assets and compares these events against predefined owner preferences. When an IoT event matches the owner's criteria, it is classified as a major event. Upon qualification, the system automatically executes default actions, including saving the event to a blockchain structure for tamper-proof recording. This ensures data integrity and provides a verifiable history of significant events. The blockchain integration enhances security by preventing unauthorized modifications, making it suitable for applications requiring high reliability, such as asset tracking, supply chain management, or regulatory compliance. The system streamlines event processing by automating the qualification and storage steps, reducing manual intervention and potential errors. The use of blockchain ensures transparency and trust in the recorded data, addressing concerns related to data manipulation in IoT environments.
14. The computer system of claim 8 , further comprising: in response to determining that the compared IoT event fails to match the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as a minor event; requesting an independent decision from a digital specialist to determine how to treat the IoT event, wherein a urgency score associated with IoT event is considered high; and in response to receiving the independent decision from the digital specialist, performing an action associated with the IoT event based on the received independent decision.
This invention relates to a computer system for managing Internet of Things (IoT) events associated with physical assets, particularly focusing on handling events that do not align with predefined owner preferences. The system monitors IoT events generated by physical assets and compares them against stored owner preferences. When an event fails to match these preferences, it is classified as a minor event, but with a high urgency score. The system then requests an independent decision from a digital specialist to determine the appropriate action for the event. Upon receiving the specialist's decision, the system executes the corresponding action. The digital specialist's role involves evaluating the event's context, urgency, and potential impact to decide whether to escalate, ignore, or take corrective measures. This approach ensures that events requiring human judgment are properly assessed while maintaining automated handling for routine cases. The system integrates real-time event processing, preference-based filtering, and human-in-the-loop decision-making to improve asset management efficiency and responsiveness.
15. A computer program product for qualifying an Internet of Things (IoT) event as a major event, comprising: one or more non-transitory computer-readable tangible storage media and program instructions stored on at least one of the one or more non-transitory computer-readable tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising: detecting an Internet of Things (IoT) event by utilizing one or more IoT sensors associated with a physical asset; comparing the detected IoT event with one or more owner preferences associated the physical asset; qualifying the compared IoT event as a major event based on the detected IoT event matching the one or more owner preferences; and computing an urgency score for each detected IoT event based on an average cost of repairs for one or more asset parts associated with the detected IoT event or a level of significance associated with the one or more asset parts associated with the detected IoT event.
This invention relates to a system for qualifying Internet of Things (IoT) events as major events based on owner preferences and asset significance. The system detects IoT events using sensors associated with a physical asset, such as machinery or equipment. The detected events are compared against predefined owner preferences, which may include thresholds or criteria for determining event importance. If the event matches these preferences, it is classified as a major event. The system further computes an urgency score for each event, considering factors like the average repair cost of affected asset parts or the significance level of those parts. This urgency score helps prioritize responses to events based on their potential impact. The invention aims to improve asset management by automating event qualification and prioritization, reducing manual intervention and ensuring timely maintenance or corrective actions. The system leverages IoT sensor data and owner-defined rules to enhance decision-making in asset monitoring and maintenance workflows.
16. The computer program product of claim 15 , further comprising: in response to determining that the compared IoT event fails to match the one or more owner preferences associated with the physical asset, qualifying the compared IoT event as a minor event; and performing one or more default choices, wherein the compared IoT event is saved in a traditional database.
This invention relates to a system for managing Internet of Things (IoT) events associated with physical assets, particularly focusing on event classification and handling based on owner preferences. The system monitors IoT events generated by physical assets and compares them against predefined owner preferences to determine whether the events require special handling or can be classified as minor events. When an IoT event does not match the owner's preferences, the system qualifies it as a minor event and performs default actions, such as storing the event in a traditional database. The system may also include a rules engine that evaluates the IoT events against the owner preferences to make these determinations. Additionally, the system may generate alerts or notifications for events that do match the owner preferences, indicating that further action may be required. The invention aims to streamline IoT event management by automating the classification and handling of events based on user-defined criteria, reducing the need for manual intervention and improving efficiency in monitoring physical assets. The system may be implemented as a computer program product, integrating with existing IoT infrastructure to enhance event processing capabilities.
17. The computer program product of claim 15 , wherein the IoT event is selected from a group consisting of: (i) an on/off trigger; (ii) a threshold IoT sensor reading; (iii) an anomaly to a predicted sensor reading; (iv) a location event; and (v) a urgency score.
This invention relates to an Internet of Things (IoT) event processing system that detects and responds to specific IoT events in a networked environment. The system monitors IoT devices and identifies events such as on/off triggers, threshold sensor readings, anomalies in predicted sensor data, location-based events, or urgency scores derived from sensor inputs. When an event is detected, the system generates an alert or triggers an automated response, such as adjusting device settings or notifying a user. The system may also analyze historical data to improve event detection accuracy over time. The invention enhances IoT system reliability by ensuring timely responses to critical conditions, reducing manual intervention, and improving operational efficiency. The event detection logic can be customized based on user-defined thresholds or machine learning models to adapt to different IoT applications, such as smart homes, industrial automation, or environmental monitoring. The system integrates with existing IoT infrastructure, allowing seamless deployment without requiring significant hardware modifications.
18. The computer program product of claim 15 , further comprising: continuously monitoring the one or more IoT sensors associated with the physical asset, wherein the one or more IoT sensors are selected from a group consisting of: (i) a current health status associated with the physical asset; (ii) a current status of one or more asset parts associated with the physical asset; (iii) an identifier of asset parts associated with the physical asset; (iv) a historical usage of the physical asset; (v) a predicted maintenance associated with the physical asset; (vi) an anomaly associated with a performance of the physical asset; (vii) any recommended preventive maintenance schedules; (viii) any failure predictions associated with the physical asset; and (ix) one or more digital twins associated with a function and the performance of the physical asset.
This invention relates to a computer program product for monitoring and managing physical assets using Internet of Things (IoT) sensors. The system addresses the challenge of efficiently tracking asset health, performance, and maintenance needs in real-time to prevent failures and optimize operations. The program continuously monitors IoT sensors associated with a physical asset, collecting data on various parameters. These parameters include the current health status of the asset, the status of its individual parts, identifiers for those parts, historical usage data, predicted maintenance requirements, performance anomalies, recommended preventive maintenance schedules, failure predictions, and digital twin models representing the asset's function and performance. The digital twin models simulate the asset's behavior to enhance predictive analytics. By integrating these diverse data points, the system enables proactive maintenance, reduces downtime, and improves asset lifecycle management. The continuous monitoring ensures timely detection of issues, allowing for early intervention before failures occur. This approach leverages IoT sensor data to provide a comprehensive view of asset conditions, supporting data-driven decision-making for maintenance and operational efficiency.
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November 25, 2019
March 29, 2022
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